Application of Artificial Neural Networks and Support Vector Machines for carbonate pores size estimation from 3D seismic data
محل انتشار: مجله معدن و محیط زیست، دوره: 4، شماره: 1
سال انتشار: 1392
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 609
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شناسه ملی سند علمی:
JR_JMAE-4-1_004
تاریخ نمایه سازی: 13 مرداد 1394
چکیده مقاله:
This paper proposes a method for the prediction of pore size values in hydrocarbon reservoirs using 3D seismic data. To this end, an actual carbonate oil field in the south-western part of Iran was selected. Taking real geological conditions into account, different models of reservoir were constructed for a range of viable pore size values. Seismic surveying was performed next on these models. From seismic response of the models, a large number of seismic attributes were identified as candidates for pore size estimation. Classes of attributes such as energy, instantaneous, and frequency attributes were included amongst others. Applying sensitivity analysis, we determined Instantaneous Amplitude and asymmetry as the two most significant attributes. These were subsequently used in our machine learning algorithms. In particular, we used feed-forward artificial neural networks (FNN) and support vector regression machines (SVR) to develop relationships between the known attributes and pore size values in a given setting. The FNN consists of twenty one neurons in a single hidden layer and the SVR method uses a Gaussian radial basis function. Compared with real values from the well data, we observed that SVM performs better than FNN due to its better handling of noise and model complexity
کلیدواژه ها:
نویسندگان
A Alimoradi
Faculty of Mining, Petroleum and Geophysics, ShahroodUniversity of Technology; Shahrood, Iran
A Moradzadeh
Faculty of Mining, Petroleum and Geophysics, ShahroodUniversity of Technology; Shahrood, Iran
M. R. Bakhtiari
Department of Petroleum Engineering, Amir Kabir University of Technology (Tehran Polytechnic), Tehran, Iran